A multi-focus image fusion algorithm in 5G communications

The traditional RSS-based fingerprint localization algorithm needs RSS values from all access points (AP) at each reference point (RP). In the large-scale indoor environment, the increasing of the number of APs will lead to establish a large-scale fingerprint database, which occupies a lot of storage space. In this paper, we propose a new reliable localization algorithm, which firstly utilizes quantized RSS to encode the monitoring region which has been divided into grids, so as to specify the grids that the interested target appears roughly. Then, we utilize Multi-Layer Perceptron (MLP) to train the grid regions in which the beacons deployment is non-isomorphic and obtain the accurate localization result. Due to the same deployment of isomorphic regions, it is imperative to train only one model to replace the others, which greatly reduces the computation of neural network. It can be concluded from the experimental results that compared with the traditional MLP-based fingerprint localization algorithm, the proposed algorithm reduces the size of fingerprint database over 80% with guarantee of localization accuracy. Moreover, our algorithm can obtain better localization accuracy compared with the other latest quantization based localization algorithm. INDEX TERMS Grid coding, multi-layer perceptron, indoor localization.

Section: A Quantized Rss Grid Coding Based On Minkowski Distancementioning

confidence: 99%

A Reliable Localization Algorithm Based on Grid Coding and Multi-Layer Perceptron

Sun¹,

Zhang

Ren³

2020

“…WVD has many good time-frequency characteristics. Considering that window functions are not included in equations (11) and (12), it avoids the contradiction between the time resolution and frequency resolution of STFT. Its timebandwidth product can reach the lower bound given by the Heisenberg uncertainty principle.…”

Section: Wigner-ville Distributionmentioning

confidence: 99%

Research on the Internet of Things Device Recognition Based on RF-Fingerprinting

Zhang

et al. 2019

Internet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The radio frequency fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust principle component analysis (RPCA) is used for the dimensionality reduction and the support vector machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real IoT equipment environment.INDEX TERMS Internet of Things, radio frequency identification, robust principle component analysis, support vector machines.

“…Even if the data generated from same sources while they can still represent different forms [1]- [7]. Therefore, data processing techniques become particularly important in many areas, such as data transmission [8]- [12], restoration [13],image fusion [14],and feature extraction [2]- [4], [15]- [19] and so on. In the real world, an object…”

Section: Introductionmentioning

confidence: 99%

Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint

Ding

Chen

et al. 2020

The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is usually presented for cross-view multimedia data. The intrinsic imperfection leads disappointing performance for cross-view multimedia recognition or classification. To solve this problem, in this paper, we propose a novel discriminative learning framework with low-rank constraint, which can be applied for view-invariant low-dimensional subspace learning. The advantages of our framework include three aspects. Firstly, to unlock the latent class structure and view structure, a self-expressed model by dual low-rank constraints are presented, which can separate the two manifold structures in the learned subspace. Secondly, two effective discriminative graphs are constructed to guide the affinity relationship of data in the above two low-dimensional projected subspaces respectively. Finally, the joint semantic consensus constraint is designed to be integrated into the learning framework, which can explore the shared and view-specific information for enforcing the view-invariant character in semantic space. Experimental results on several public cross-view multimedia datasets demonstrate that our proposed method outperforms existing excellent subspace learning approaches. INDEX TERMS Multimedia analysis, multi-view discriminative analysis, cross-view feature learning, lowrank representation.